A Bayesian spam filter determines an amount of content in incoming email messages that it knows from training. If the filter is familiar with a threshold amount of the content, then the filter proceeds to classify the email message as being spam or legitimate. On the other hand, if not enough of the words in the email are known to the filter from training, then the filter cannot accurately determine whether or not the message is spam. In this case, the filter classifies the message as being of type unknown. Different threshold metrics can be used, such as the percentage of known words, and the percentage of maximum correction value used during processing. This greatly improves the processing of emails in languages on which the filter was not trained.